Residential and commercial sectors - COFFEE-TEA: Difference between revisions

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The energy consumption is actually driven by the energy services required by the households. The IEA database [[CiteRef::iea2014]] was used to estimate the energy consumption in the buildings (residential and commercial) sector. The energy services modelled are: Space Heating, Water Heating, Cooking, Lighting, Cooling (Ambient Conditioning) and Appliances. For these services the energy service intensity was calculated relative to the total estimated buildings as a first indicator of the energy demand estimation. Non-electricity-based technology participation for lighting, such as kerosene, biomass and candles was also estimated. For ambient heating and cooling the average HDD and CDD for every region were estimated.

Latest revision as of 11:58, 6 September 2019

Alert-warning.png Note: The documentation of COFFEE-TEA is 'under review' and is not yet 'published'!

Model Documentation - COFFEE-TEA

Corresponding documentation
Previous versions
Model information
Model link
    Institution COPPE/UFRJ (Cenergia), Brazil, http://www.cenergialab.coppe.ufrj.br/.
    Solution concept General equilibrium (closed economy)
    Solution method The COFFEE model is solved through Linear Programming (LP). The TEA model is formulated as a mixed complementary problem (MCP) and is solved through Mathematical Programming System for General Equilibrium -- MPSGE within GAMS using the PATH solver.
    Anticipation

    The energy consumption is actually driven by the energy services required by the households. The IEA database iea2014 was used to estimate the energy consumption in the buildings (residential and commercial) sector. The energy services modelled are: Space Heating, Water Heating, Cooking, Lighting, Cooling (Ambient Conditioning) and Appliances. For these services the energy service intensity was calculated relative to the total estimated buildings as a first indicator of the energy demand estimation. Non-electricity-based technology participation for lighting, such as kerosene, biomass and candles was also estimated. For ambient heating and cooling the average HDD and CDD for every region were estimated.

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